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Confocal Raman Imaging andCorrelative Techniquesin Life Sciences

Confocal Raman microscopy is apromising technique for highresolution, non-destructive andlabel-free analysis of cells and theirmain components. It is ideally suitedfor correlative imaging.

The Raman principle

No need for compromises

The Raman effect is based on the inelastic
scattering of light by the molecules ofgaseous, liquid or solid materials. Theinteraction of a molecule with photons
causes vibrations of its chemical bonds,
leading to specific energy shifts in the
scattered light. Thus, any given chemicalcompound produces a particular Raman
spectrum when excited and can be easily
identified by this individual “fingerprint.”
Raman spectroscopy is a well-established,label-free and non-destructive method foranalyzing the molecular composition of asample.

The Raman effect is extremely weak, so
every Raman photon is important for
imaging. Therefore WITec Raman imaging
systems combine an exceptionallysensitive confocal microscope with anultra-high throughput spectrometer(UHTS). Precise adjustment of all opticaland mechanical elements guarantees thehighest resolution, outstanding speed andextraordinary sensitivity - simultaneously!
This optimization allows the detectionof Raman signals of even weak Raman
scatterers and extremely low material
concentrations or volumes with thelowest excitation energy levels. This isan unrivaled advantage of WITec systems.

Raman imaging
In Raman imaging, a confocal microscopeis combined with a spectrometer anda Raman spectrum is recorded at every
image pixel. The resulting Raman image
visualizes the distribution of the sample’s
compounds. Due to the high confocality of
WITec Raman systems, volume scans and3D images can also be generated.

Resolution
Lateral resolution is physicallylimited to ~200 nm, dependingon the wavelength of theincident light.

Speed
The more sensitive a system is, the shorter
the acquisition time for a single spectrum.
WITec’s Ultrafast Raman Imaging reduces
acquisition times for single Raman spectra
down to well below 1 ms.

Sensitivity
A high confocality increases the signal-to-noise ratio by reducing
the background. With the UHTS Series, WITec developed lens-based,
wavelength-optimized spectrometers with a spectral resolution
down to 0.1 cm-1 relative wavenumbers.

Confocal Raman imaging of living cells
Being a non-destructive microscopy
technique, confocal Raman imaging iswell suited for the study of living cells intheir physiological surroundings without
damaging them.
Here, epithelial rat cells were investigatedwith a WITec alpha300 R Raman microscope system. After a suitable cell was
identified (A), complete Raman spectra
were acquired at every pixel of the image(scan range: 40 x 40 µm2, 100 x 100 pixels,
10,000 spectra). The integral intensity of
the C-H stretching band was used to generate a Raman image of the cell (B).
Of all spectra obtained, three, shown in(C), were identified as being typical for
mitochondria (blue), endoplasmic reticulum (green) and nucleoli (red). Based onthese spectra, a Raman image was generated with the corresponding colors (D). The
endoplasmic reticulum and other different
parts of the cell are clearly visible. Even the
nuclear membrane is well-defined. Thus,
using confocal Raman imaging, a "color-labeled" image can be created without
dyeing the sample.

For extensive coverage of Raman
imaging in life sciences see the book
Confocal Raman Microscopy
DOI 10.1007/078-3-319-76380-5,
Springer Int. Publishing, 2018.

A

B

C

D

Figure 1: Raman imaging of a living rat cell(A) Video image. (B) Intensity in the C-H stretching band (2800 cm-1 – 3000 cm-1). (C) Determinationof basic spectra from mitochondria (blue), endoplasmic reticulum (green) and nucleoli (red). (D)Color-coded Raman image generated from the basic spectra.
Sample courtesy of Angelika Rück, ILM, Ulm, Germany.

Raman imaging reveals components of atherosclerotic human aorta
Healthy and diseased tissues differ fromeach other with regard to cellular components. Raman spectral imaging can beused to identify and locate these differences. This intriguing capability of Raman
imaging has been demonstrated extensively, i.e. in studies on cellular differentiation (Nat. Chomm. 8, 148843, 2017, J. Chemometrics 22, 42973, 2018), mitosis, apoptosis
and tissue regeneration (Regenerative
Med. 2, 12-21, 2017) and in many models of
diseases such as cancer (Anal. Chim. Acta
909, 91-100, 20169, hypertension (Analyst
140, 2178-2184, 2015), inflammation andadiposis (Sci. Rep. 7, 1-10, 2017; Analyst 143,
5999, 2018)

A

B

Here (Fig. 2) we present data from a study
by Amanda You and colleagues at the Imperial College London (Sci. Adv. 3, e1701156,
2017).
To elucidate the differences between calcification and atherosclerosis the scientists
have conducted an extensive Raman study
on nonatherosclerotic and atherosclerotic
human aorta.
Raman spectral data of cross sections
through the blood vessels revealed several
characteristic Raman bands. Using spectral
demixing nine main components could be
identified by their typical Raman spectra(A): elastin, collagen, cholesterol and its
esters, triglycerides, ß-carotene and two
mineral species: apatite and whitlockite.

These spectra were used to image the distribution of the respective components incross sections of a healthy and an atherosclerotic aorta. Classic histological dyes
were used as controls.
Changes in composition such as highconcentrations of triglycerides and cholesterol ester and lack of ß-carotene in theatherosclerotic aorta could be detected inatherosclerotic plaques and also at distantloci of the aorta. Despite limitations suchas missing of biochemical compounds "We
have unearthed significant new insights
into the characterization of aortic tissue
through the use of Raman spectroscopy,"
conclude the scientists who conducted thestudy.

C

Figure 2: Raman imaging of calcified, artherosclerotic aorta
Demixed Raman spectra for nine major components of aortic tissues (A) and representative
images from nonatherosclerotic and atherosclerotic (plaque) aorta (B) along with associatedhistology (C). Scale bars= 100 µm.
Modified from Amanda You et al., Sci. Adv. 3,
e1701156, 2017

Identification and discrimination of cells with Raman imaging
Recent studies have shown that Raman
microscopy can be used to discriminate
between malignant and healthy cells.
As an example, we present data from astudy performed by Halina Abramzcyk
and Beata Brozek-Pluska (Analyst 140,
2134-2143., 2015).
They examined healthy and cancerous
human breast cell tissue of a patientwith an alpha300 R confocal Raman
microscope. They found the main difference in the concentration of carotinoids (Fig. 5): healthy tissue contained
a substantial amount of carotinoids(A), whereas they were almost absent

in tumor tissue (B). Carotinoids were alsoabsent in the breast ducts of cancerous
tissue, monounsaturated fatty acids werereduced, but the amount of saturated
lipids and sphingomyelin increased. This
molecular pattern could serve as a marker
for breast cancer diagnosis.
Raman signatures were also identified for
medullablastoma cells (Child‘s Nervous
System 34, 2425-2430, 2018), brain cancer(Spectrochim. Acta A 188, 8-19, 2018) andallows monitoring angiogenesis duringcancer development (Spectrochim. Acta A
198, 338-345, 2018). These and other studies
may pave the way for "Raman cytology" incancer diagnosis.

A

C

B

D

Raman cell studies are not restricted totumor tissue. Surmacki and colleagues
demonstrated that Raman imaging issensitive and specific enough to identify
different living human lung cell lines inculture (Sci. Rep. 8, 12604, 2018). Others also
showed that this technique is suitable toiffderentiate other cell types, i.e. CHO hostfrom producer cell lines (Biotechnol. J. 12,2017).

Figure 3: Raman analysis of tumor tissue
Breast tissue from the margin of the tumor mass (A) and tumor mass (carcinoma
ductale G3 infiltrans mammae) (B).
Based on the Raman spectra (C, D) color-coded Raman images were developed:
carotinoids (yellow) could only be identified in healthy tissue and was missing intumorous tissue. Color codes for spectra(C, D): yellow: carotinoids (Raman bands
1490 – 1580 cm-1), red: proteins (2900 –
3010 cm-1), purple: fluorescence, blue: lipids(2850 – 2950 cm-1)
Images courtesy of Halina Abramczyk andBeata Brozek-Pluska, Laboratory of Laser
Molecular Spectroscopy, Lodz University ofTechnology, Poland.

Yes you can! Combine fluorescence microscopy with confocal Raman imaging
The use of fluorescent markers is a well-established and widely-used technique in life
sciences for locating and imaging specificmolecules. Raman microscopy, however,allows the investigation of all the molecular components of a sample, not only
those prepared with fluorescent markers.
This is despite the fact that fluorescence isgenerally regarded as the worst enemy ofRaman imaging. Why? Because the Raman
effect is a very weak effect, fluorescenceis 100 to 1000 times stronger. So, can you
acquire a Raman image from a fluorescing
sample?
Yes, you can!

Here we demonstrate that Raman imaging
is possible on cells that have also beendyed with DAPI (Fig. 4).
DAPI is a fluorescent molecule that specifically stains nuclei of eukaryotic cells (A). Bymeans of specific Raman spectra (C), theendoplasmic reticulum (red) and nucleoli
inside the nuclei (green) were identified.
An overlay of the confocal Raman image
and the DAPI stain is shown in (B). These
images were acquired with the confocal,
inverted alpha300 Ri Raman microscope,
which is well suited for imaging samples
in aqueous solutions.

A

B

Figure 4: Correlative Raman – fluorescence microscopy imaging of eukaryotic cells

C(A) Nuclei were stained with DAPI (blue). (B) Endoplasmic reticulum (red) and nucleoli (green) were identified by their Raman signals. (C) Raman spectra
corresponding to nucleoli (green) and endoplasmic reticulum (red).
Sample courtesy of Claudia Scalfi-Happ ILM, Ulm, Germany.

Another experiment was carried out on
Chinese hamster ovary (CHO) cells thatwere fixed on a microscope slide (Fig. 5).
The cells, identified via video imaging (A),expressed the green fluorescent protein(GFP) in their mitochondria (B).
The same cells were analyzed in confocal
Raman imaging mode. A Raman spectrum
was acquired at every pixel of the image,summing up to 14,000 spectra. In the color-coded Raman image the cell body, thecytoplasm (green), the nucleus (yellow)
and the nucleoli (pink) can be clearly distinguished (C). Note that GFP fluorescence
did not interfere with Raman imaging.
In post-processing, the Raman spectra
were subjected to cluster analysis. For
this procedure, the data was automatically compared and similar spectra weregrouped into one cluster each. The spectra
are plotted in (D). Thus, a simplified Raman
image could be developed (E) in which thecytoplasm is shown in blue, the nucleus in
red, nucleoli in green and lipids in yellow.

A

C

B

Figure 5: Correlative fluorescence and Raman
microscopy of eukaryotic cells
From a culture of Chinese hamster ovary (CHO)
cells, a single cell was chosen as seen in the C
video
image (A). The cells stably expressed GFP in the
mitochondria (B). Raman spectra were then acquired at every pixel of the sample (not shown)and from them a color-coded Raman image was
generated (C). Furthermore, a cluster analysis of
the Raman data was performed (D): spectra that
identify proteins are shown in blue, those of thenucleus are in red, nucleoli in green and lipids inyellow. The cluster analysis is shown in (E).
Images courtesy of Claudia Scalfi-Happ ILM, Ulm,
Germany.

D

E

Studying the life of macrophages
The monitoring of living cells is essential
for unraveling dynamic processes in cells
and tissues. Raman studies showing theuptake of lipids into macrophages in realtime were successfully performed by thegroup of Jürgen Popp at the Leibniz-Institute of Photonic Technology in Jena.
Macrophages take up low density lipoproteins (LDL) for recycling and removal. If
their export capabilities are overextended,

the cells store lipids and develop into foamcells. These foam cells contribute to cardiovasular diseases such as athereosclerosis.
With real time Raman imaging it was possible to follow the dynamics of lipid uptake
into macrophages observed over a periodof more than 35 hours, revealing striking
cell-to-cell-heterogeneity (J. Biophotonics
10, 1-10, 2017).

In previous studies, the uptake of lipids into
macrophages was studied on fixed cells.
Oleic acid was labelled with deuterium. For
quantification of its uptake, C-D stretching vibrations at 2050 – 2275 cm-1 were
evaluated. The images clearly show thatthe lipid concentration increases over time
and that the fatty acid is stored in droplets(Fig. 6). The appearance of lipid droplets is ahallmark of foam cell formation.

Figure 6: Raman imaging of macrophages
Raman images of macrophage cells aquired by integrating over the 2401 cm-1 C-D stretching band of deuterium-marked oleic acid (red) and the C-H stretching band at 2800 - 3020 cm-1 (blue). After 30 hours of incubation time, the cells store excessive lipids and develop into foam cells.
Images courtesy of Christian Matthäus, Leibniz Institute of Photonic Technology, Jena, Germany.

Bacteria in the focus of Raman microscopy
Raman imaging has also proven very useful in examining the microbial world. With
this technique, bacteria species can be distinguished and their physiological status
and growth as well as their response to
drug treatment can be analyzed.
Here, a strain of Bacillus cereus was investigated (Fig. 7). The video image (A) shows apart of the bacterial colony. Raman microscopy (B) distinguishes between vegetative
cells without (yellow) or with the storage
polymer polyhydroxybutyrate PHP (green)from spores (magenta). The generative
cells can only develop after the bacteria
have grown to the stationary phase andaccumulated enough PHB.
The fact that all three components can beeasily detected highlights the diagnostic
power of confocal Raman imaging forA

identifying phenotypic heterogeneity at asingle-cell level.

B

Figure 7: Raman imaging of Bacillus cereus(A) Video image of growing Bacillus cereus. (B) Raman image from the region indicated by the rectangle in (A) showing vegetative cells without PHB in yellow, with PHB in green and spores in purple.
Images courtesy of Antje Hermelink, Robert-Koch-Institute, Berlin, Germany.

3D Raman imaging of a cellIn this study provided by Katarzyna Majzner and colleagues Raman imaging wasused to three dimensionally investigate
the heterogeneity of single endothelial
cells and to establish the size, volume, shape and biochemical composition of theircellular organelles.
The ability of confocal Raman imaging to
generate 3D maps without disrupting the
spatial integrity of the cell provides uniqueinsight into biochemical architecture andcellular processes of the endothelium.
3D confocal Raman imaging can be carriedout over subsequent measurements ofseveral sample layers. It can also be used
e.g. for the early diagnosis of cancer bydetecting subtle biochemical changes incells and tissues associated with cancer
development and progression or for morphological analysis of tumors.
An alpha300 R confocal Raman microscope with a 60x water-immersion objective
for cells was used for this study. All spectra
were baseline corrected using a 3rd degree
polynomial and the routine procedure forremoval of cosmic rays was applied. The
Image J processing program was applied
to obtain 3D pictures of cells and tissues.
The results of this and similar studies(Analyst 138, 603-610, 2012; Nature Comm.
8, 14843, 2017) document that 3D Raman
imaging is a label-free method thatprovides unique insight into the three-dimensional distribution and localization ofmolecular components of a cell.

A

B

Figure 8: Confocal Raman imaging-stack of an EA.hy 926 endothelial cell(A) 2D Integration maps over the CH stretching vibration (2800–3020 cm-1 range) at different z-positions with corresponding single spectra (B) extracted from the same point in the maps (dotted line).
Images courtesy of Katarzyna Majzner and Malgorzata Baranska, Faculty of Chemistry,
Jagiellonian University, Ingardena 3, 30-060 Krakow, Poland.

Characterization of woody plant cells

The cell walls of woody plant tissues arecomplex, multi-layered structures. Theircharacteristic feature is the developmentof secondary cell layers. These layers
strengthen and thicken the primary cellwall on which they are based after thecell has reached its final size and shape.
Primary cell walls consist primarily of carbohydrates (cellulose, hemicellulose andpectin). Secondary cell walls incorporate
more carbohydrates. Cellulose is depositedas stiff fibrils that are embedded in moreductile hemicellulose and lignin. Ordinarily,
secondary cell walls contain three layers,
S1 – S3, that vary in thickness and chemicalcomposition.
The S2-layer, which accounts for 75 - 85
percent of the total secondary cell wall
thickness, is most important for the mechanical stability of the plants‘ stems andbranches. The middle lamella is attachedto the adjacent primary cell walls. It ensures the adhesion of a cell to adjacent cells.
Here (Fig. 9) confocal Raman microscopy
was used to illustrate the molecular composition of tension wood in hybrid black
poplar (Populus nigra x Populus deltoides).
Tension (reaction) wood is produced in response to environmental influences suchas wind. It develops in young wood, before
the secondary cell wall is completed. It
consists of the historically-named gelatinous layer or G-layer and a small S2-layer.
In this experiment, a cross section (20 µm
thick) was investigated in water using thealpha300 R confocal Raman microscopeby integrating over defined spectral areas.
The intensity of the aromatic lignin band
(1550 – 1650 cm-1) that is characteristic forlignin is highest in the cell corners and thecompound middle lamella, and lower in
the S-layers (A). The G-layer can be identified by integrating over the C-H stretching
Raman band (2810 – 2940 cm-1) that
identifies cellulose as the main component of this layer (b). Lignin could not be
detected in this layer. The S-layers andthe G-layer show an intense Raman band

B

A

C

Figure 9: Raman imaging of young wood of Populus nigra x Populus deltoides in water(A-C) Raman images of a cross section (14 x 14 µm2) generated by integrating over different Ramanlines. Intensities of (A) the aromatic lignin band (1550 – 1650 cm-1), (B) the C-H stretching band (2810– 2935 cm-1) and (C) the C-C/C-O stretching bands (1026-195 cm-1). Raman spectra that are typical forthe components of secondary cell walls (D) were used to create a color-coded Raman image of thesection (E), indentifying the G-layer (red), the S2-layer (green) and the middle lamella and cell corners(yellow).

at 1026 - 1195 cm-1, which is typical for
carbohydrates.
Three spectra that can distinguish the layers (D) were used to develop a color-coded
Raman image of the wood cell (E).
Detailed Raman analyses of various woody
tissues were published by Nothburga
Gierlinger and colleagues (Planta 227, 969- 980, 2008; Plant J. 61, 854 - 861, 2010; Tree
Physiol. 38, 1526-1537, 2018, Nano Lett. 20,
2647-2653, 2020).

E

D

RISETM microscopy: Correlative Raman imaging and scanning electron microscopy
Confocal Raman imaging identifies andlocalizes the molecular compounds of asample. Scanning electron microscopy(SEM) reveals the morphology and surface
structure of the object under investigationfrom the millimeter range down to atomic
resolution.
The combination of both techniques in
one instrument enables the correlation ofchemical and structural features. The Raman measurements are performed within
the vacuum chamber of an SEM. A precisetranslation stage automatically transfers
samples from one measuring position tothe other, thus streamlining the workflow
and drastically improving ease of use. We
named this type of correlative analysis
RISE™ microscopy.
Here we show the application of RISE™
microscopy on three biological samples: across-section of hamster brain (Fig. 10), thesurface of a pearl (Fig. 11) and a polishedcross-section of an abalone shell (Fig. 12).

A

B

Figure 10: Hamster brain imaged with RISE™ microscopy(A) RISE image of the brain tissue sample. In the color-coded Raman image the white brain matter isshown in green and the gray matter in red. (B) Raman spectra reveal the different spectral characteristics of the white and gray matter of a hamster brain slice.

A

B

Figure 11: RISE™ microscopy image of a pearl

Figure 12: RISE™ microscopy image of an abalone shell

Confocal Raman imaging indicates that thepearl consists of two polymorphs of calcium
carbonate, namely aragonite (blue, red) andvaterite (green). Structural features were imaged
with SEM. The correlation of the information ofboth techniques results in the RISE™ image.

Mother-of-pearl consists of several crystal forms of aragonite. They can be distinguished by their
Raman spectra (A): the different crystal orientations are shown in blue and red. The RISE™ image (B)illustrates how the anisotropy is correlated with the tructural features of the material.

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